The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
Xiamen Vangenes Biotechnology Co., Ltd, Xiamen, 361015, Fujian, China.
Clin Epigenetics. 2024 Feb 27;16(1):33. doi: 10.1186/s13148-024-01641-x.
Whole-genome methylation sequencing of cfDNA is not cost-effective for tumor detection. Here, we introduce reduced representative methylome profiling (RRMP), which employs restriction enzyme for depletion of AT-rich sequence to achieve enrichment and deep sequencing of CG-rich sequences.
We first verified the ability of RRMP to enrich CG-rich sequences using tumor cell genomic DNA and analyzed differential methylation regions between tumor cells and normal whole blood cells. We then analyzed cfDNA from 29 breast cancer patients and 27 non-breast cancer individuals to detect breast cancer by building machine learning models.
RRMP captured 81.9% CpG islands and 75.2% gene promoters when sequenced to 10 billion base pairs, with an enrichment efficiency being comparable to RRBS. RRMP allowed us to assess DNA methylation changes between tumor cells and whole blood cells. Applying our approach to cfDNA from 29 breast cancer patients and 27 non-breast cancer individuals, we developed machine learning models that could discriminate between breast cancer and non-breast cancer controls (AUC = 0.85), suggesting possibilities for truly non-invasive cancer detection.
We developed a new method to achieve reduced representative methylome profiling of cell-free DNA for tumor detection.
全基因组 cfDNA 甲基化测序在肿瘤检测方面不具有成本效益。在这里，我们引入了简化代表性甲基化谱分析（RRMP），该方法采用限制性内切酶来耗尽富含 AT 的序列，以实现 CG 丰富序列的富集和深度测序。
我们首先使用肿瘤细胞基因组 DNA 验证 RRMP 富集 CG 丰富序列的能力，并分析肿瘤细胞与正常全血细胞之间的差异甲基化区域。然后，我们分析了 29 名乳腺癌患者和 27 名非乳腺癌个体的 cfDNA，通过构建机器学习模型来检测乳腺癌。
RRMP 在测序到 100 亿个碱基对时捕获了 81.9%的 CpG 岛和 75.2%的基因启动子，其富集效率与 RRBS 相当。RRMP 使我们能够评估肿瘤细胞和全血细胞之间的 DNA 甲基化变化。将我们的方法应用于 29 名乳腺癌患者和 27 名非乳腺癌个体的 cfDNA，我们开发了可以区分乳腺癌和非乳腺癌对照的机器学习模型（AUC=0.85），这表明真正的非侵入性癌症检测是可能的。
我们开发了一种新的方法来实现用于肿瘤检测的游离细胞 DNA 的简化代表性甲基化谱分析。
Zotero 插件
